“You know you are building a platform if your users are using it in ways you have never imagined”

The goal of the platform is to enable interactions between producers and consumers – repeatedly and efficiently.

Platforms are ubiquitous.

Quotes from Geoffrey G. Parker

In recent years, more and more businesses are shifting from the pipeline structure to the platform structure. In this shift, the simple pipeline arrangement is transformed into a complex relationship in which producers, consumers, and the platform itself enter into a variable set of relationships.

Yet all are operating businesses that share the fundamental platform DNA—they all exist to create matches and facilitate interactions among producers and consumers, whatever the goods being exchanged may be.

The shift from protecting value inside the firm to creating value outside the firm means that the crucial factor is no longer ownership but opportunity, while the chief tool is no longer dictation but persuasion.

Platform competition requires treating buyers and suppliers not as separate threats to be subjugated but as value-creating partners to be wooed, celebrated, and encouraged to play multiple roles.

And while platform businesses themselves are often extraordinarily profitable, the chief locus of wealth creation is now outside rather than inside the organization.

Thus, every platform business must be designed to facilitate the exchange of information.

Yet, in most cases, platforms don’t create value units; instead, they are created by the producers who participate in the platform. Thus, platforms are “information factories” that have no control over inventory.

Facebook’s news feed is a classic multiuser feedback loop. Status updates from producers are served to consumers, whose likes and comments serve as feedback to the producers. The constant flow of value units stimulates still more activity, making the platform increasingly valuable to all participants.

Later still, LinkedIn created another interaction when it allowed thought leaders, and subsequently all users, to publish posts on LinkedIn for others to read, effectively turning the site into a publishing platform.

A platform’s overarching purpose is to consummate matches among users and facilitate the exchange of goods, services, or social currency, thereby enabling value creation for all participants.

As a result of the rise of the platform, almost all the traditional business management practices—including strategy, operations, marketing, production, research and development, and human resources—are in a state of upheaval. We are in a disequilibrium time that affects every company and individual business leader. The coming of the world of platforms is a major reason why.

Our goal is not to build a platform; it is to be across all of them.

~ Mark Zuckerberg

We want to be the platform that solution providers can use to run their businesses. ~ Bob Vogel

Have you ever wondered where the centre of a city is? Where do those milestones you see on a highway which tend to (->) 0 actually reach 0? How does GPS measure distances? Well, I took it upon myself to find out!

What other city than our Bangalore? namma Bengaluru?

I wanted to figure out where the centre of Bangalore / Bengaluru is! I searched for Google for the Lat/ Long Co-ordinates of this great city – this is what I found

Brilliant, so she is at a Latitude of 12.9716 and a Longitude of 77.5946.

Now, let us Plot!

I am using the map visualization package called ‘leaflet’ to plot. More details on this package at this link.

It is not a lot of code to plot using the leaflet package, a snapshot of RStudio with the code and the generated plot below –

Look at the location, are you surprised? Well, I was. I was expecting it to be at the junction of old city (Chickpet) or some place near City Market or Chamarajpet!

If someone from the future were to communicate to you (from another dimension using say Gravity 🙂 Interstellar style) and ask you to be in the centre of Bangalore, now you know where to wait! You just need to know when!

About the Mahabharata

The Mahabharata is the longest known epic poem and has been described as “the longest poem ever written”.Its longest version consists of over 100,000 shlokas or over 200,000 individual verse lines (each shloka is a couplet), and long prose passages. About 1.8 million words in total, the Mahabharata is roughly ten times the length of the Iliad and the Odyssey combined, or about four times the length of the Ramayana.

The first section of the Mahabharata states that it was Ganesha who wrote down the text to Vyasa’s dictation. Ganesha is said to have agreed to write it only if Vyasa never paused in his recitation. Vyasa agrees on condition that Ganesha takes the time to understand what was said before writing it down.

The Epic is divided into a total of 18 Parvas or Books.

Well, if Rama was at the centre of Ramayana, who was the equivalent in Mahabharata? Krishna? One of the Pandavas? One of the Kauravas? Dhritarashtra? Or one of the queens – Draupadi? Kunti? Gandhari?

Let us find out –

Since, this is a continuation to the first blog in this series, I would not take you through the intricacies of downloading and installing packages. Also, there is a Rpdf that needs to be installed, you could lookup on the instructions in this link.

Download and copy the pdf onto a folder in the local file system. You may want to read the pdf in its entirety to a corpus.

The next step would be to create a TermDocumentMatrix, a matrix that lists all the occurrences of words in the corpus. The DTM represents the documents as rows and the words as columns, if a word occurs in a particular document, the matrix entry corresponding to that row and column is 1 or it is a 0. Multiple occurrences are then added to the same count.

A WordCloud in R

Let Noble thoughts come to us from every side

– Rigveda, I-89-i

Have you ever wondered what it would be to do a textual analysis of some ancient texts? Would it not be nice to ‘mine’ insights into Valmiki’s Ramayana? Or Veda Vyasa’s Mahabharata? The Ramayana arguably happened about 9300 years ago. In the Thretha yuga. The wiki for Ramayana.

So, there are a total of 24,000 verses in total. Well, I don’t really have the pdf of the ‘Original’ version, I thought I could use C. Rajagopalachari’s English retelling of the epic. This particular book is quiet popular and has sold over a million copies. It is a page-turner and has around 300 pages.

How about analyzing the text in this book?

Wouldn’t it be EPIC?!

That is exactly what I want to embark on this blog, text mining helps to derive valuable insights into the mind of the writer. It can also be leveraged to gain in-tangible insights like sentiment, relevance, mood, relations, emotion, summarization etc.

The first part of this series would be to run a descriptive analysis on the text and generate a word cloud. Tag clouds or word clouds add simplicity and clarity, the most used words are displayed as weighted averages, the more the count of the word, bigger would be the size of the word. After all, isn’t it visually engaging than looking at a table?

Firstly, we would need to install the relevant packages in R and load them –

The second step would be to read the pdf (which is currently in my working directory)

I first validate if the pdf is there in my working directory

The ‘tm’ package just provides a readPDF function, but the pdf engine needs to be downloaded. Let us use a pdf engine called xpdf. The link for setting up the pdf engine (and updating the system path) is here.

Great, now we can get rolling.

Let us create a pdf reader called ‘Rpdf’ using the code below, this instructs the pdftotext.exe to maintain the original physical layout of the text.

> Rpdf <- readPDF(control = list(text = "-layout"))

Now, we might need to convert the pdf to text and store it in a corpus. Basically we need to instruct the function on which resource we need to read. The second parameter is the reader that we created in the previous line.

If I look at the summary of the variable, it will prompt me with the following details.

The next step would be to do some transformation on the text, let us use the tm_map() function is to replace special characters from the text. We could use this to replace single quotes (‘), full stops (.) and replace them with spaces.

Also, don’t you think we need to remove all the stop words? Words like ‘will’, ‘shall’, ‘the’, ‘we’ etc. do not make much sense in a word cloud. These are called stopwords, the tm_map provides for a function to do such an operation.

> ramayana <- tm_map(ramayana, removeWords, stopwords("english"))

Let us also convert all the text to lower

> ramayana <- tm_map(ramayana, content_transformer(tolower))

I could also specify some stop-words that I would want to remove using the code:

Any other pre-processing that you can think of? How about removing suffixes, removing tense in words? Is ‘kill’ different from ‘killed’? Do they not originate from the same stem ‘kill’? Or ‘big’, ‘bigger’, ‘biggest’? Can’t we just have ‘big’ with a weight of 3 instead of these three separate words? We use the stemDocument parameter for this.

> ramayana <- tm_map(ramayana, stemDocument)

The next step would be to create a term-document matrix. It is a table containing the frequency of words. We use ‘termdocumentmatrix’ provided by the text mining package to do this.

This really brings us to the package to be discussed on this blog – dplyr. The CRAN documentation for dplyr can be found here.

For this blog, I would be demonstrating the 5 operations of the package. The first thing we would need is to install the package and load the library.

> install.packages(“dplR”)

> library(dplR)

We then need to find a dataset on which we could run these operations. CRAN makes the download logs of their packages publicly available here – CRAN package download logs. Let us download the file for July 8, 2014 (we could really pick a log from any date) onto RStudio’s working directory.

Once the file has been copied onto the working directory of R, execute the below line (where the variable path2csv stores the location of the csv)

> mydf <- read.csv(path2csv, stringsAsFactors = FALSE)

we then save the data frame onto a variable called cran by converting it to a tbl_df to improve readability. Calling the variable cran prints out the contents.

> cran <- tbl_df(mydf)

> cran

The dplyr philosophy is to have small functions that do one thing well. There are basically 5 commands that cover most of the fundamental data manipulation tasks.

select()

Usually in the entire data set that we use for analyis, we would really be interested in a few columns. This function is used to select / fetch the columns which are required. If I only need the columns ip_id, package and country. I execute the following statement –

> select(cran, ip_id, package, country)

It is important to note that the columns are returned in the order in which we specified, irrespective of how it was in the original dataframe.

We could also use the ‘-‘ sign to ommit the columns we do not need.

> select(cran, -time)

filter()

Now that we know how to select columns, the next logical thing would be to be able to select rows. That is where the filter() function comes in.

This is like the ‘where’ clause in SQL. Let us understand this by an example –

> filter(cran, package == "swirl")

If you look at the column ‘package’, we now see that the resulting dataframe has only rows which have the package as ‘swirl’.

Multiple conditions can be passed to filter() one after the other. For example, if I want to fetch all swirl packages downloaded on the OS – linux in India:

This is used to order the rows of a dataset according to the values of a particular variable. Suppose we want to order all rows of a dataset in ascending / descending order of a column. Notice the ip_id column listed in descending order.

> arrange(cran2, desc(ip_id))

mutate()

This function is used to edit or add additional columns to the dataframe. Suppose I want to convert the size column which is in bytes to megabytes and store the values in a column called size_mb.

> mutate(cran3, size_mb = size / 2^20)

sumarize()

This function is used to collapse the dataset into a single row, the go-to function to calculate the mean in a sanitized dataframe.

For example – I want to know the average download size from the size column.

> summarize(cran, avg_bytes = mean(size))

sumarize() can also be used to fetch records in groups using the FOR EACH construct.

Disclosure: The above example is from the dplyR lesson on the swirl package.

It has been years since I bought this domain. I finally managed to get this hosted (on a shared hosting space from my friend). This has been on my bucket list for this year, happy to have ticked it before the year ends (BTW, I have two different bucket lists – one for the financial year ending (for my financial goals) and the other for the Julian Calendar). I just realized writing this that I put two sets of brackets in my previous statement. Well, I shall let that pass for now.

Coming to the purpose of this site (Indian English?). I intend to use this as a personal blog/ space. My digital presence. My online avatar. My springboard into the web. My Hangar. It would also be the one-stop-shop (no, no, don’t start thinking of e-comm, I do not intend to sell anything here) for all the information that I would really want to share with the world out there. To establish a digital presence, it looks like I need the following –

Content – Shall build it in a few days

Strategy – Interesting, Strategy for a personal blog? this needs some thought

Design – I totally intend to use come customized templates, I also would use this to play around with some UI/ UX